Adaptive neural PD controllers for mobile manipulator trajectory tracking.

Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller.

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution.

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Emotional system in complex cognitive activities of working memory: A literature review of its role.

Cognitive processing is needed to elicit emotional responses. At the same time, emotional responses modulate and guide cognition to enable adaptive responses to the environment. However, most empirical studies and theoretical models of cognitive functions have been investigated without taking into account emotion, which is considered interference that is counterproductive to the correct functioning of the cognitive system. To understand how complex behaviors are carried out in the brain, an understanding of the interactions between emotion and cognition may be indispensable. Given the enormous scope of this topic for both cognition and emotion, these concepts will not be further defined here; instead, this review will be relatively narrow in scope and will emphasize several brain systems involved in the interactions between emotion and working memory because an important dimension of cognition involves working memory function. In attempting to understand the relationship between emotion and working memory, we will describe the projections of a set of brain structures that support our emotional life and the neuromodulator dopamine (which is also involved in emotion processing and incentive motivational behavior) in the prefrontal cortex. According to the literature, working memory engages the cortical regions. Thus, the prefrontal cortex, particularly the dorsolateral prefrontal cortex (DLPFC), although commonly viewed as a purely cognitive area, provides a test for the hypothesis that working memory and emotion are strongly integrated in the brain. In this review, we provide an overview of neuropsychological, neuroanatomical and molecular evidence, with the aim of establishing the extent to which working memory and emotion are related.

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Crystallization of zeolites from organo-silicic colloids.

As shown recently, the networks of mesoporous high-surface-area silicates and zeolites undergo a deep depolymerization process in glycerol, near 200 degrees C. Within 1 h, X-ray diffraction analysis amorphous gels are obtained. However, some local ordering subsists as demonstrated by a striking similarity between the silicon and aluminum high-resolution solid-state NMR spectra before and after the reaction. The residual organization could be investigated indirectly in studying the recrystallization of these gels in the presence or absence of structure-directing agents. Were this attempt successful, the way should be opened for the synthesis of molecular sieves starting from gels obtained from naturally occurring zeolites. Here, it will be shown that an amorphous gel obtained from HZSM-5 recovers the initial long-range structure of the parent material in a few hours at 85 degrees C in the presence of an aqueous solution of tetrapropyl ammonium (TPA) or NH3. The recrystallization of HY requires the presence of tetramethylammonium, but about 25% of the crystallization is obtained rapidly (approximately = 1 day) at 80 degrees C with ammonia. Hypotheses about the preorganized structural units are presented. The value of the Si-O-Si angle in the silica cluster seems to be of paramount importance.